Inertial sensors such as gyroscopes and accelerometers are used in a variety of applications for measuring motion in vehicle navigation, in oil field drilling, and so on. These inertial sensors are often inside an inertial navigation system (INS) and then combined with yet other instruments such as a Global Positioning System (GPS) that are mounted on a vehicle such as an aircraft. The raw GPS/INS calculation of the aircraft's position, velocity, attitude, etc., may be corrected through Kalman filtering. The filter estimates errors by fusing the raw data and uncertainties from the various instruments that measure position, velocity, attitude, and heading. There may be uncertainties and errors for the raw measurements from the gyroscopes and accelerometers. Even the relatively precise GPS measurements may have some uncertainties and errors.
The errors estimated by a Kalman filter may be due to artifacts. For example, when external disturbances affect the aircraft due to turbulence or acoustic noise (e.g. explosion) or internal disturbances due to electronics issues, these disturbances may cause the instruments such as the accelerometer to realize larger DC bias offset values and/or rapid changes in the DC bias values. Then the Kalman filter that estimates the errors may produce shifted results for a period of time. During, and for some time after the disturbance, the Kalman filter is still governed by the small error uncertainties that it estimated prior to the disturbance and therefore may assign artificially small errors to states associated with the disturbance. Accordingly, the Kalman filter will continue for some time duration to over-weight the raw data garnered during the disturbance, and produce a poor estimate of the state solution. Moreover, because the Kalman filter is operating on a plurality of inputs, the Kalman filter might not know which parameters are most affected and require correction.